Xie MJ, Li JJ, Guo YJ, Wang Q, Tan ZB, Li YL, Li JP. Construction of a prognostic model for colorectal cancer liver metastasis: A retrospective study based on population data. World J Gastrointest Oncol 2025; 17(11): 110675 [DOI: 10.4251/wjgo.v17.i11.110675]
Corresponding Author of This Article
Ji-Peng Li, Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Medical University, No. 127 Changle West Road, Xincheng District, Xi’an 710032, Shaanxi Province, China. jipengli1974@aliyun.com
Research Domain of This Article
Psychology, Clinical
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Nov 15, 2025 (publication date) through Nov 13, 2025
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Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Oncology
ISSN
1948-5204
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Xie MJ, Li JJ, Guo YJ, Wang Q, Tan ZB, Li YL, Li JP. Construction of a prognostic model for colorectal cancer liver metastasis: A retrospective study based on population data. World J Gastrointest Oncol 2025; 17(11): 110675 [DOI: 10.4251/wjgo.v17.i11.110675]
Mian-Jiao Xie, Ji-Peng Li, Department of Experimental Surgery, Xijing Hospital, Air Force Medical University, Xi’an 710032, Shaanxi Province, China
Jia-Jun Li, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, Chengdu 610000, Sichuan Province, China
Ya-Jie Guo, Qi Wang, Zhao-Bang Tan, Yun-Long Li, Ji-Peng Li, Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Medical University, Xi’an 710032, Shaanxi Province, China
Co-first authors: Mian-Jiao Xie and Jia-Jun Li.
Co-corresponding authors: Yun-Long Li and Ji-Peng Li.
Author contributions: Li JP and Li YL designed the study; Xie MJ and Li JJ jointly completed the statistical analysis and manuscript preparation; Guo YJ, Wang Q and Tan ZB helped perform the analysis with constructive discussions. All authors contributed to the article and approved the submitted version. Xie MJ and Li JJ declare that they contributed equally to this work and should be considered co-first authors. They were involved in all aspects of the study, including study design, data analysis, and manuscript preparation. Similarly, Li JP and Li YL provided equal guidance and mentorship throughout the research process and should be considered co-corresponding authors. They were responsible for the overall direction and execution of the study, as well as final approval of the manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Xijing Hospital, the First Affiliated Hospital of Air Force Military Medical University (Approval number: KY20232168-F-1). This study, which is in compliance with the Helsinki Declaration, obtained informed consent from all participants.
Informed consent statement: All data sources obtained in this study received informed consent from the patients.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original contributions presented in the study are included in the article/Supplementary material. Further inquiries can be directed to the corresponding authors at jipengli1974@aliyun.com.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ji-Peng Li, Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Medical University, No. 127 Changle West Road, Xincheng District, Xi’an 710032, Shaanxi Province, China. jipengli1974@aliyun.com
Received: June 12, 2025 Revised: July 18, 2025 Accepted: October 9, 2025 Published online: November 15, 2025 Processing time: 155 Days and 11.1 Hours
Abstract
BACKGROUND
Colorectal cancer (CRC) is a prevalent gastrointestinal malignancy with a typically unfavorable prognosis following the onset of liver metastases.
AIM
To develop and validate a new clinical prediction model to accurately forecast overall survival (OS) in CRC patients following surgical treatment for liver metastasis.
METHODS
This study included 1059 patients diagnosed with CRC liver metastases (CRLM) at the Xijing Hospital between 2010 and 2022. The patients were randomly divided into training and validation cohorts at a 7:3 ratio. Key clinical predictors were identified using least absolute shrinkage and selection operator (LASSO) regression combined with a Cox proportional hazards model, leading to the establishment of a prediction model and preparation of a nomogram to enhance its clinical utility. Decision curve analysis (DCA) and Kaplan-Meier survival analysis were employed to evaluate the precision and predictive performance of the model.
RESULTS
The LASSO-Cox regression analysis revealed multiple pivotal clinical biomarkers significantly linked to CRLM, including gamma-glutamyl transferase levels, blood chloride concentration, activated partial thromboplastin time, N stage, and vascular invasion. The model's receiver operating characteristic curve area under the curve exceeded 0.7 for both the training and validation groups with moderate-to-good predictive accuracy. Furthermore, DCA validated the nomogram's effectiveness for OS prediction. Kaplan-Meier risk stratification demonstrated markedly improved OS among patients classified as low-risk compared to those categorized as high-risk (P < 0.001), highlighting its clinical utility for risk assessment and treatment guidance.
CONCLUSION
The nomogram prediction model constructed in this study has good predictive value and can effectively assess the survival rate of patients with CRLM.
Core Tip: This study presents a novel nomogram for predicting overall survival in patients with colorectal cancer (CRC) liver metastasis post-surgery. In a retrospective cohort analysis of 1059 patients, we identified key prognostic factors including gamma-glutamyl transferase, blood chloride concentration, activated partial thromboplastin time, N stage, and vascular invasion. The nomogram demonstrated robust predictive accuracy with area under the curve values exceeding 0.7 in both training and validation cohorts. These findings offer a clinically relevant tool for personalized risk assessment and treatment planning in CRC liver metastasis, potentially improving patient outcomes.
